{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85aaa355",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64) (1797,)\n",
      "[[ 0.  0.  5. ...  0.  0.  0.]\n",
      " [ 0.  0.  0. ... 10.  0.  0.]\n",
      " [ 0.  0.  0. ... 16.  9.  0.]\n",
      " ...\n",
      " [ 0.  0.  1. ...  6.  0.  0.]\n",
      " [ 0.  0.  2. ... 12.  0.  0.]\n",
      " [ 0.  0. 10. ... 12.  1.  0.]] [0 1 2 ... 8 9 8]\n",
      "[[ 0.         -0.33501649 -0.04308102 ... -1.14664746 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  0.54856067 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  1.56568555  1.6951369\n",
      "  -0.19600752]\n",
      " ...\n",
      " [ 0.         -0.33501649 -0.88456568 ... -0.12952258 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -0.67419451 ...  0.8876023  -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649  1.00877481 ...  0.8876023  -0.26113572\n",
      "  -0.19600752]]\n",
      "[[ 1.91421366 -0.95450157 -3.94603482 ... -0.01797902  0.04795038\n",
      "   0.01912424]\n",
      " [ 0.58898033  0.9246358   3.92475494 ... -1.14415907  0.03774402\n",
      "   0.37167996]\n",
      " [ 1.30203906 -0.31718883  3.02333293 ...  0.48730406 -1.35695937\n",
      "  -0.10701564]\n",
      " ...\n",
      " [ 1.02259599 -0.14791087  2.46997365 ... -0.15253558  0.0509132\n",
      "   0.59550456]\n",
      " [ 1.07605522 -0.38090625 -2.45548693 ... -0.44673737 -0.46840846\n",
      "   0.16065193]\n",
      " [-1.25770233 -2.22759088  0.28362789 ...  0.43650024 -0.92270619\n",
      "  -0.06156133]]\n",
      "(1797, 34)\n",
      "直接比对预测值与真实值：\n",
      " [ True  True  True  True  True False  True False  True  True  True  True\n",
      " False  True  True False False  True  True  True  True  True  True False\n",
      " False  True  True  True  True  True False False  True  True False False\n",
      " False  True  True  True False  True False  True  True  True False False\n",
      " False  True  True  True False False  True  True False False False False\n",
      "  True  True  True  True  True False  True  True  True False  True  True\n",
      "  True  True  True  True False  True  True False  True False  True  True\n",
      " False  True  True  True  True False  True  True  True False False  True\n",
      "  True False False  True  True False False  True  True  True  True  True\n",
      "  True  True False False  True  True False  True  True False False  True\n",
      "  True  True False False False  True False  True  True False False False\n",
      "  True  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True False False  True  True False False  True  True  True  True\n",
      "  True False  True  True  True  True  True  True  True False False  True\n",
      " False False  True  True  True False  True False  True  True  True  True\n",
      "  True  True False False  True  True False False False  True False  True\n",
      "  True  True  True  True  True  True False  True  True  True  True  True\n",
      "  True False  True  True False  True False False False  True  True False\n",
      "  True False False  True False  True  True  True False  True False  True\n",
      "  True  True False False False  True False False False False  True  True\n",
      "  True  True  True  True False False  True  True  True  True False False\n",
      " False  True False  True False  True False False  True  True False  True\n",
      " False False False False  True  True  True  True  True False False  True\n",
      "  True False  True False  True False  True  True  True  True False False\n",
      "  True False  True  True  True False  True False False False  True  True\n",
      " False  True  True  True False  True  True  True  True  True  True  True\n",
      "  True  True False  True False  True False False False  True  True False\n",
      " False  True  True  True  True  True  True  True  True  True  True False\n",
      " False  True  True False  True  True  True  True False  True False False\n",
      "  True  True False False False  True False False  True  True  True  True\n",
      "  True  True False  True  True  True False False False  True  True False\n",
      "  True  True False False  True  True  True  True  True False False  True\n",
      "  True  True  True False  True  True  True  True False  True  True False\n",
      "  True  True False False  True  True  True False  True False  True  True\n",
      " False False False  True False  True  True False False False False False\n",
      "  True  True False False False  True False  True  True  True  True  True\n",
      "  True  True False  True  True  True False False False False  True  True\n",
      " False False  True  True  True  True]\n",
      "准确率为：\n",
      " 0.6288888888888889\n",
      "this is a test file!\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[42  1  0  0  0  0  1  0  1  7]\n",
      " [ 0 35  1  0  0  1  0  0  2  3]\n",
      " [ 0  0 13  0  0  0  0  4 32  2]\n",
      " [ 0  0  0 21  0  1  0  0  9  9]\n",
      " [ 0  5  0  0 36  0  0  4  4  2]\n",
      " [ 0  1  0  0  0 19  0  0 20 11]\n",
      " [ 0 10  1  0  0  0 31  0  2  1]\n",
      " [ 0  0  1  0  1  0  0 33  8  1]\n",
      " [ 0  2  1  1  0  2  0  1 30  2]\n",
      " [ 2  3  0  2  0  1  0  0  4 23]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.81      0.88        52\n",
      "           1       0.61      0.83      0.71        42\n",
      "           2       0.76      0.25      0.38        51\n",
      "           3       0.88      0.53      0.66        40\n",
      "           4       0.97      0.71      0.82        51\n",
      "           5       0.79      0.37      0.51        51\n",
      "           6       0.97      0.69      0.81        45\n",
      "           7       0.79      0.75      0.77        44\n",
      "           8       0.27      0.77      0.40        39\n",
      "           9       0.38      0.66      0.48        35\n",
      "\n",
      "    accuracy                           0.63       450\n",
      "   macro avg       0.74      0.64      0.64       450\n",
      "weighted avg       0.76      0.63      0.65       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from common.utils import plot_learning_curve\n",
    "import seaborn as sn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "\n",
    "def show(image):\n",
    "    test = image.reshape((8, 8))  # 从一维变为二维，这样才能被显示\n",
    "    print(test.shape)  # 查看是否是二维数组\n",
    "    # print(test)\n",
    "    plt.imshow(test, cmap=plt.cm.gray)  # 显示灰色图像\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def standard_demo(data):\n",
    "    transfer = StandardScaler()\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def pca_demo(data):\n",
    "    transfer = PCA(n_components=0.92)\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def Adab_demo(data, label):\n",
    "    # 划分数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(data, label, random_state=6)\n",
    "    # 训练模型\n",
    "    estimate = AdaBoostClassifier(n_estimators=100, learning_rate=0.01)\n",
    "    estimate.fit(X_train, y_train)  # 模型构建好了\n",
    "    # 模型评估的两种方法：1：直接比对预测值与真实值；\n",
    "    y_predict = estimate.predict(X_test)\n",
    "    print(\"直接比对预测值与真实值：\\n\", y_test == y_predict)\n",
    "    # 2：计算准确率\n",
    "    score = estimate.score(X_test, y_test)\n",
    "    print(\"准确率为：\\n\", score)\n",
    "    # 绘制学习曲线!!!!\n",
    "    cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)  # 10折交叉验证\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10, 6), dpi=144)\n",
    "    plot_learning_curve(ax, estimate, \"Learn Curve\",\n",
    "                        X_train, y_train, ylim=(0.0, 1.01), cv=cv)\n",
    "    plt.show()\n",
    "    # 混淆矩阵\n",
    "    cm = confusion_matrix(y_test, y_predict)\n",
    "    print(cm)\n",
    "    # 可视化显示混淆矩阵\n",
    "    # annot = True 显示数字 ，fmt参数不使用科学计数法进行显示\n",
    "    ax = sn.heatmap(cm, annot=True, fmt='.20g')\n",
    "    ax.set_title('confusion matrix')  # 标题\n",
    "    ax.set_xlabel('predict')  # x轴\n",
    "    ax.set_ylabel('true')  # y轴\n",
    "    plt.show()\n",
    "    # 计算精确率与召回率\n",
    "    report = classification_report(y_test, y_predict, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "                                   target_names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
    "    print(report)\n",
    "\n",
    "# 按间距中的绿色按钮以运行脚本。\n",
    "if __name__ == '__main__':\n",
    "    # 查看数据集，以图片显示\n",
    "    print(X.shape, y.shape)\n",
    "    print(X, y)\n",
    "    # show(X[1795])\n",
    "    # 数据集预处理（标准化、特征降维）\n",
    "    X_new = standard_demo(X)\n",
    "    X_new = pca_demo(X_new)\n",
    "    print(X_new.shape)  # 从64维降到了40维\n",
    "    # 机器学习建模\n",
    "    # 调参\n",
    "    # 模型评估\n",
    "    # 学习曲线\n",
    "    Adab_demo(X_new, y)  # 数据已经准备好了\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3f54520",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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